| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System.Collections.Generic;
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| 23 | using System.Linq;
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| 24 | using HeuristicLab.Analysis;
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| 25 | using HeuristicLab.Core;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.Problems.DataAnalysis.Evaluators;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 34 |
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| 35 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
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| 36 | /// <summary>
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| 37 | /// An operator that analyzes the validation best scaled symbolic regression solution.
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| 38 | /// </summary>
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| 39 | [Item("FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer", "An operator that analyzes the validation best scaled symbolic regression solution.")]
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| 40 | [StorableClass]
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| 41 | public sealed class FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer : SingleSuccessorOperator, ISymbolicRegressionAnalyzer {
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| 42 | private const string RandomParameterName = "Random";
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| 43 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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| 44 | private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 45 | private const string ProblemDataParameterName = "ProblemData";
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| 46 | private const string ValidationSamplesStartParameterName = "SamplesStart";
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| 47 | private const string ValidationSamplesEndParameterName = "SamplesEnd";
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| 48 | // private const string QualityParameterName = "Quality";
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| 49 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 50 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 51 | private const string EvaluatorParameterName = "Evaluator";
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| 52 | private const string MaximizationParameterName = "Maximization";
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| 53 | private const string BestSolutionParameterName = "Best solution (validation)";
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| 54 | private const string BestSolutionQualityParameterName = "Best solution quality (validation)";
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| 55 | private const string CurrentBestValidationQualityParameterName = "Current best validation quality";
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| 56 | private const string BestSolutionQualityValuesParameterName = "Validation Quality";
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| 57 | private const string ResultsParameterName = "Results";
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| 58 | private const string VariableFrequenciesParameterName = "VariableFrequencies";
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| 59 | private const string BestKnownQualityParameterName = "BestKnownQuality";
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| 60 | private const string GenerationsParameterName = "Generations";
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| 61 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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| 62 |
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| 63 | private const string TrainingMeanSquaredErrorQualityParameterName = "Mean squared error (training)";
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| 64 | private const string MinTrainingMeanSquaredErrorQualityParameterName = "Min mean squared error (training)";
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| 65 | private const string MaxTrainingMeanSquaredErrorQualityParameterName = "Max mean squared error (training)";
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| 66 | private const string AverageTrainingMeanSquaredErrorQualityParameterName = "Average mean squared error (training)";
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| 67 | private const string BestTrainingMeanSquaredErrorQualityParameterName = "Best mean squared error (training)";
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| 68 |
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| 69 | private const string TrainingAverageRelativeErrorQualityParameterName = "Average relative error (training)";
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| 70 | private const string MinTrainingAverageRelativeErrorQualityParameterName = "Min average relative error (training)";
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| 71 | private const string MaxTrainingAverageRelativeErrorQualityParameterName = "Max average relative error (training)";
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| 72 | private const string AverageTrainingAverageRelativeErrorQualityParameterName = "Average average relative error (training)";
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| 73 | private const string BestTrainingAverageRelativeErrorQualityParameterName = "Best average relative error (training)";
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| 74 |
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| 75 | private const string TrainingRSquaredQualityParameterName = "R² (training)";
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| 76 | private const string MinTrainingRSquaredQualityParameterName = "Min R² (training)";
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| 77 | private const string MaxTrainingRSquaredQualityParameterName = "Max R² (training)";
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| 78 | private const string AverageTrainingRSquaredQualityParameterName = "Average R² (training)";
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| 79 | private const string BestTrainingRSquaredQualityParameterName = "Best R² (training)";
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| 80 |
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| 81 | private const string TestMeanSquaredErrorQualityParameterName = "Mean squared error (test)";
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| 82 | private const string MinTestMeanSquaredErrorQualityParameterName = "Min mean squared error (test)";
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| 83 | private const string MaxTestMeanSquaredErrorQualityParameterName = "Max mean squared error (test)";
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| 84 | private const string AverageTestMeanSquaredErrorQualityParameterName = "Average mean squared error (test)";
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| 85 | private const string BestTestMeanSquaredErrorQualityParameterName = "Best mean squared error (test)";
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| 86 |
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| 87 | private const string TestAverageRelativeErrorQualityParameterName = "Average relative error (test)";
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| 88 | private const string MinTestAverageRelativeErrorQualityParameterName = "Min average relative error (test)";
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| 89 | private const string MaxTestAverageRelativeErrorQualityParameterName = "Max average relative error (test)";
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| 90 | private const string AverageTestAverageRelativeErrorQualityParameterName = "Average average relative error (test)";
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| 91 | private const string BestTestAverageRelativeErrorQualityParameterName = "Best average relative error (test)";
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| 92 |
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| 93 | private const string TestRSquaredQualityParameterName = "R² (test)";
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| 94 | private const string MinTestRSquaredQualityParameterName = "Min R² (test)";
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| 95 | private const string MaxTestRSquaredQualityParameterName = "Max R² (test)";
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| 96 | private const string AverageTestRSquaredQualityParameterName = "Average R² (test)";
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| 97 | private const string BestTestRSquaredQualityParameterName = "Best R² (test)";
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| 98 |
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| 99 | private const string RSquaredValuesParameterName = "R²";
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| 100 | private const string MeanSquaredErrorValuesParameterName = "Mean squared error";
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| 101 | private const string RelativeErrorValuesParameterName = "Average relative error";
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| 102 |
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| 103 | #region parameter properties
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| 104 | public ILookupParameter<IRandom> RandomParameter {
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| 105 | get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
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| 106 | }
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| 107 | public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 108 | get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 109 | }
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| 110 | public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
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| 111 | get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
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| 112 | }
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| 113 | public ILookupParameter<ISymbolicRegressionEvaluator> EvaluatorParameter {
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| 114 | get { return (ILookupParameter<ISymbolicRegressionEvaluator>)Parameters[EvaluatorParameterName]; }
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| 115 | }
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| 116 | public ILookupParameter<BoolValue> MaximizationParameter {
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| 117 | get { return (ILookupParameter<BoolValue>)Parameters[MaximizationParameterName]; }
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| 118 | }
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| 119 | public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
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| 120 | get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
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| 121 | }
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| 122 | public IValueLookupParameter<IntValue> ValidationSamplesStartParameter {
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| 123 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesStartParameterName]; }
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| 124 | }
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| 125 | public IValueLookupParameter<IntValue> ValidationSamplesEndParameter {
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| 126 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesEndParameterName]; }
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| 127 | }
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| 128 | public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 129 | get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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| 130 | }
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| 131 |
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| 132 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 133 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 134 | }
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| 135 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 136 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 137 | }
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| 138 | public ILookupParameter<SymbolicRegressionSolution> BestSolutionParameter {
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| 139 | get { return (ILookupParameter<SymbolicRegressionSolution>)Parameters[BestSolutionParameterName]; }
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| 140 | }
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| 141 | public ILookupParameter<IntValue> GenerationsParameter {
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| 142 | get { return (ILookupParameter<IntValue>)Parameters[GenerationsParameterName]; }
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| 143 | }
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| 144 | public ILookupParameter<DoubleValue> BestSolutionQualityParameter {
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| 145 | get { return (ILookupParameter<DoubleValue>)Parameters[BestSolutionQualityParameterName]; }
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| 146 | }
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| 147 | public ILookupParameter<ResultCollection> ResultsParameter {
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| 148 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
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| 149 | }
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| 150 | public ILookupParameter<DoubleValue> BestKnownQualityParameter {
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| 151 | get { return (ILookupParameter<DoubleValue>)Parameters[BestKnownQualityParameterName]; }
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| 152 | }
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| 153 | public ILookupParameter<DataTable> VariableFrequenciesParameter {
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| 154 | get { return (ILookupParameter<DataTable>)Parameters[VariableFrequenciesParameterName]; }
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| 155 | }
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| 156 |
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| 157 | #endregion
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| 158 | #region properties
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| 159 | public IRandom Random {
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| 160 | get { return RandomParameter.ActualValue; }
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| 161 | }
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| 162 | public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
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| 163 | get { return SymbolicExpressionTreeParameter.ActualValue; }
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| 164 | }
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| 165 | public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
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| 166 | get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
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| 167 | }
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| 168 | public ISymbolicRegressionEvaluator Evaluator {
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| 169 | get { return EvaluatorParameter.ActualValue; }
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| 170 | }
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| 171 | public BoolValue Maximization {
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| 172 | get { return MaximizationParameter.ActualValue; }
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| 173 | }
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| 174 | public DataAnalysisProblemData ProblemData {
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| 175 | get { return ProblemDataParameter.ActualValue; }
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| 176 | }
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| 177 | public IntValue ValidiationSamplesStart {
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| 178 | get { return ValidationSamplesStartParameter.ActualValue; }
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| 179 | }
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| 180 | public IntValue ValidationSamplesEnd {
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| 181 | get { return ValidationSamplesEndParameter.ActualValue; }
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| 182 | }
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| 183 | public PercentValue RelativeNumberOfEvaluatedSamples {
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| 184 | get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
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| 185 | }
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| 186 |
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| 187 | public DoubleValue UpperEstimationLimit {
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| 188 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 189 | }
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| 190 | public DoubleValue LowerEstimationLimit {
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| 191 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 192 | }
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| 193 | public ResultCollection Results {
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| 194 | get { return ResultsParameter.ActualValue; }
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| 195 | }
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| 196 | public DataTable VariableFrequencies {
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| 197 | get { return VariableFrequenciesParameter.ActualValue; }
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| 198 | }
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| 199 | public IntValue Generations {
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| 200 | get { return GenerationsParameter.ActualValue; }
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| 201 | }
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| 202 | public DoubleValue BestSolutionQuality {
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| 203 | get { return BestSolutionQualityParameter.ActualValue; }
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| 204 | }
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| 205 |
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| 206 | #endregion
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| 207 |
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| 208 | public FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer()
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| 209 | : base() {
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| 210 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
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| 211 | Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
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| 212 | Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
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| 213 | Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
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| 214 | Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
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| 215 | Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
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| 216 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set."));
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| 217 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set."));
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| 218 | Parameters.Add(new ValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1)));
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| 219 | Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper estimation limit that was set for the evaluation of the symbolic expression trees."));
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| 220 | Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower estimation limit that was set for the evaluation of the symbolic expression trees."));
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| 221 | Parameters.Add(new LookupParameter<SymbolicRegressionSolution>(BestSolutionParameterName, "The best symbolic regression solution."));
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| 222 | Parameters.Add(new LookupParameter<IntValue>(GenerationsParameterName, "The number of generations calculated so far."));
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| 223 | Parameters.Add(new LookupParameter<DoubleValue>(BestSolutionQualityParameterName, "The quality of the best symbolic regression solution."));
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| 224 | Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored."));
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| 225 | Parameters.Add(new LookupParameter<DoubleValue>(BestKnownQualityParameterName, "The best known (validation) quality achieved on the data set."));
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| 226 | Parameters.Add(new LookupParameter<DataTable>(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts"));
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| 227 | }
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| 228 |
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| 229 | [StorableConstructor]
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| 230 | private FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(bool deserializing) : base() { }
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| 231 |
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| 232 | [StorableHook(HookType.AfterDeserialization)]
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| 233 | private void AfterDeserialization() {
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| 234 | #region compatibility remove before releasing 3.3.1
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| 235 | if (!Parameters.ContainsKey(EvaluatorParameterName)) {
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| 236 | Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
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| 237 | }
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| 238 | if (!Parameters.ContainsKey(MaximizationParameterName)) {
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| 239 | Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
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| 240 | }
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| 241 | #endregion
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| 242 | }
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| 243 |
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| 244 | public override IOperation Apply() {
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| 245 | var trees = SymbolicExpressionTree;
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| 246 |
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| 247 | string targetVariable = ProblemData.TargetVariable.Value;
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| 248 |
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| 249 | // select a random subset of rows in the validation set
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| 250 | int validationStart = ValidiationSamplesStart.Value;
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| 251 | int validationEnd = ValidationSamplesEnd.Value;
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| 252 | uint seed = (uint)Random.Next();
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| 253 | int count = (int)((validationEnd - validationStart) * RelativeNumberOfEvaluatedSamples.Value);
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| 254 | if (count == 0) count = 1;
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| 255 | IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers(seed, validationStart, validationEnd, count);
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| 256 |
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| 257 | double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
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| 258 | double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
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| 259 |
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| 260 | double bestQuality = Maximization.Value ? double.NegativeInfinity : double.PositiveInfinity;
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| 261 | SymbolicExpressionTree bestTree = null;
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| 262 |
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| 263 | foreach (var tree in trees) {
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| 264 | double quality = Evaluator.Evaluate(SymbolicExpressionTreeInterpreter, tree,
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| 265 | lowerEstimationLimit, upperEstimationLimit,
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| 266 | ProblemData.Dataset, targetVariable,
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| 267 | rows);
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| 268 |
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| 269 | if ((Maximization.Value && quality > bestQuality) ||
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| 270 | (!Maximization.Value && quality < bestQuality)) {
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| 271 | bestQuality = quality;
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| 272 | bestTree = tree;
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| 273 | }
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| 274 | }
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| 275 |
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| 276 | // if the best validation tree is better than the current best solution => update
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| 277 | bool newBest =
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| 278 | BestSolutionQuality == null ||
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| 279 | (Maximization.Value && bestQuality > BestSolutionQuality.Value) ||
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| 280 | (!Maximization.Value && bestQuality < BestSolutionQuality.Value);
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| 281 | if (newBest) {
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| 282 | // calculate scaling parameters and only for the best tree using the full training set
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| 283 | double alpha, beta;
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| 284 | int trainingStart = ProblemData.TrainingSamplesStart.Value;
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| 285 | int trainingEnd = ProblemData.TrainingSamplesEnd.Value;
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| 286 | IEnumerable<int> trainingRows = Enumerable.Range(trainingStart, trainingEnd - trainingStart);
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| 287 | SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(SymbolicExpressionTreeInterpreter, bestTree,
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| 288 | lowerEstimationLimit, upperEstimationLimit,
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| 289 | ProblemData.Dataset, targetVariable,
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| 290 | trainingRows, out beta, out alpha);
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| 291 |
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| 292 | // scale tree for solution
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| 293 | var scaledTree = SymbolicRegressionSolutionLinearScaler.Scale(bestTree, alpha, beta);
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| 294 | var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(),
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| 295 | scaledTree);
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| 296 | var solution = new SymbolicRegressionSolution(ProblemData, model, lowerEstimationLimit, upperEstimationLimit);
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| 297 | solution.Name = BestSolutionParameterName;
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| 298 | solution.Description = "Best solution on validation partition found over the whole run.";
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| 299 |
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| 300 | BestSolutionParameter.ActualValue = solution;
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| 301 | BestSolutionQualityParameter.ActualValue = new DoubleValue(bestQuality);
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| 302 |
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| 303 | BestSymbolicRegressionSolutionAnalyzer.UpdateBestSolutionResults(solution, ProblemData, Results, Generations, VariableFrequencies);
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| 304 | }
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| 305 |
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| 306 |
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| 307 | if (!Results.ContainsKey(BestSolutionQualityValuesParameterName)) {
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| 308 | Results.Add(new Result(BestSolutionQualityValuesParameterName, new DataTable(BestSolutionQualityValuesParameterName, BestSolutionQualityValuesParameterName)));
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| 309 | Results.Add(new Result(BestSolutionQualityParameterName, new DoubleValue()));
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| 310 | Results.Add(new Result(CurrentBestValidationQualityParameterName, new DoubleValue()));
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| 311 | }
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| 312 | Results[BestSolutionQualityParameterName].Value = new DoubleValue(BestSolutionQualityParameter.ActualValue.Value);
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| 313 | Results[CurrentBestValidationQualityParameterName].Value = new DoubleValue(bestQuality);
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| 314 |
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| 315 | DataTable validationValues = (DataTable)Results[BestSolutionQualityValuesParameterName].Value;
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| 316 | AddValue(validationValues, BestSolutionQualityParameter.ActualValue.Value, BestSolutionQualityParameterName, BestSolutionQualityParameterName);
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| 317 | AddValue(validationValues, bestQuality, CurrentBestValidationQualityParameterName, CurrentBestValidationQualityParameterName);
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| 318 | return base.Apply();
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| 319 | }
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| 320 |
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| 321 | [StorableHook(HookType.AfterDeserialization)]
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| 322 | private void Initialize() { }
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| 323 |
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| 324 | private static void AddValue(DataTable table, double data, string name, string description) {
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| 325 | DataRow row;
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| 326 | table.Rows.TryGetValue(name, out row);
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| 327 | if (row == null) {
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| 328 | row = new DataRow(name, description);
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| 329 | row.Values.Add(data);
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| 330 | table.Rows.Add(row);
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| 331 | } else {
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| 332 | row.Values.Add(data);
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| 333 | }
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| 334 | }
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| 335 | }
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| 336 | }
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